VQscanning

VQscanning is a technique used in image processing and computer vision for image compression and analysis. It is based on the principle of vector quantization, where data is represented by a codebook of representative vectors. In VQscanning, an image is divided into blocks, and each block is treated as a vector. These vectors are then compared to a pre-defined codebook of vectors, and the index of the closest matching vector in the codebook is used to represent the image block. This process significantly reduces the amount of data required to represent the image.

The codebook is typically generated through a training process using a large dataset of images. Algorithms like the LBG (Linde-Buzo-Gray) algorithm are commonly used for codebook generation. During the compression phase, each image block is encoded by finding its nearest neighbor in the codebook and storing the corresponding index. During decompression, the indices are used to retrieve the representative vectors from the codebook, reconstructing an approximation of the original image. The quality of compression depends on the size and quality of the codebook, as well as the size of the image blocks.

VQscanning has applications in various fields, including digital image compression, pattern recognition, and image retrieval. While it offers good compression ratios, it can be computationally intensive, especially during the codebook generation and encoding stages. Its effectiveness is often compared to other compression techniques, with trade-offs between compression ratio, image quality, and computational complexity.